1. Technical Field of the Invention
The present invention relates to a technology for emphasizing (typically, separating or extracting) or suppressing a specific sound in a mixture of sounds.
2. Description of the Related Art
Each sound in a mixture of a plurality of sounds (voice or noise) emitted from separate sound sources is individually emphasized or suppressed by performing sound source separation on a plurality of observed signals that a plurality of sound receiving devices produce by receiving the mixture of the plurality of sounds. Learning according to Independent Component Analysis (ICA) is used to calculate a separation matrix used for sound source separation of the observed signals.
For example, a technology in which a separation matrix of each of a plurality of frequencies (or frequency bands) is learned using Frequency-Domain Independent Component Analysis (FDICA) is described in Japanese Patent Application Publication No. 2006-84898. Specifically, a time series of observed vectors of each frequency extracted from each observed signal is multiplied by a temporary separation matrix of the frequency to perform sound source separation, and the separation matrix is then repeatedly updated by learning so that the statistical independency between signals produced through sound source separation is maximized. A technology in which the amount of calculation is reduced by excluding (i.e., terminating learning of) frequencies, at which a small change is made to the accuracy of separation in the course of learning, from subsequent learning target frequencies is described in Japanese Patent Application Publication. No. 2006-84898.
However, FDICA requires a large-capacity storage unit that stores the time series of observed vectors of each of the plurality of frequencies. Although terminating the learning of separation matrices of frequencies at which the accuracy of separation undergoes little change reduces the amount of calculation, the technology of Japanese Patent Application Publication No. 2006-84898 requires a large-capacity storage unit to store the time series of observed vectors for all frequencies since learning of the separation matrix is performed for every frequency when the learning is initiated.